Import the data

## An object of class Seurat 
## 51279 features across 4197 samples within 3 assays 
## Active assay: SCT (14676 features, 2000 variable features)
##  3 layers present: counts, data, scale.data
##  2 other assays present: RNA, HTO
##  2 dimensional reductions calculated: pca, umap

Here’re some representation

General conditions

Clusteres at res0.1

Clonotypes distributions

Cell cycle distribution





Cells inspection

I’ll inspect the expression of some markers to determine the cell types

This will help us determine any cluster that may not be of interest ! (clusters at res 0.1)

T lymphocytes

Other cell types

Basically here, we’re looking for

  • Monocytes & macrophages (CD14, LYZ)
  • NK cells (NKG7, GNLY)
  • B Lymphocytes (CD19, CD27)

As for the feature plots :





Clonotypes inspection

Now we’ll inspect the distribution of the clonotypes

Let’s group unique and low-count clones together

Now let’s see the distribution among the conditions and the clusters

Clones among conditions

Clones among clusters





Filtering cells

Now let’s filter unwanted cells

# define cells to remove :
# ---------------------- :
cells_rm <- WhichCells(
  SeuratObj, 
  expression = umap_1 > 5 | 
    Clonotype %in% c("unique","low_count")
)

# subset seurat object :
# -------------------- :
SeuratObj_filt <- subset(SeuratObj, cells = setdiff(Cells(SeuratObj),cells_rm))

# rejoin conditions and clusters :
# ------------------------------ :
SeuratObj_filt@meta.data <- SeuratObj_filt@meta.data %>% 
  mutate(MyClusters = case_when(
    umap_2 < -1 & Condition == "Relapse" ~ "R",
    umap_2 < -1 & Condition == "Diagnosis" ~ "D_in_R",
    umap_2 > -1 & Condition == "Relapse" ~ "R_in_D",
    umap_2 > -1 & Condition == "Diagnosis" ~ "D"
  ))

# plot :
# ---- :
DimPlot(SeuratObj_filt,
        group.by = "MyClusters",
        cols = c(MyPalette, colors(distinct = T)),
        pt.size = 1,
        alpha = .7,
        label = T,
        label.box = T,
        label.color = "#fff",
        repel = T)+
  My_umap_theme()+
  scale_fill_manual(values = MyPalette)

Set a threshold of 30 cells minimum to be kept for further analysis (so any cluster below 30 will be filtered again)

Removing Rel_in_Diag & Diag_in_Rel

TCR inspections

Now, let’s inspect the VDJ expression :

  • Among our conditions
  • Among the clonotypes





Cell cycle inspection

Now, let’s inspect the cell cycle of the TCRs :

Now, let’s inspect the cell cycle of the clonotypes :

Now, let’s dive deeper

We’ll inspect the cell cycle within the 3 major clonotypes (cl1, cl2, cl3) against each TCR

Clonotype 1

Clonotype 2

Clonotype 3





Analysis duration : 51s

Time : 12:54:49

Session info

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.4.2 (2024-10-31 ucrt)
##  os       Windows 11 x64 (build 26100)
##  system   x86_64, mingw32
##  ui       RTerm
##  language (EN)
##  collate  French_France.utf8
##  ctype    French_France.utf8
##  tz       Europe/Paris
##  date     2025-07-26
##  pandoc   3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
##  quarto   NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
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